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+<html>
+<head>
+<title>
+Netlab Reference Manual rbfgrad
+</title>
+</head>
+<body>
+<H1> rbfgrad
+</H1>
+<h2>
+Purpose
+</h2>
+Evaluate gradient of error function for RBF network.
+
+<p><h2>
+Synopsis
+</h2>
+<PRE>
+
+g = rbfgrad(net, x, t)
+[g, gdata, gprior] = rbfgrad(net, x, t)
+</PRE>
+
+
+<p><h2>
+Description
+</h2>
+<CODE>g = rbfgrad(net, x, t)</CODE> takes a network data structure <CODE>net</CODE>
+together with a matrix <CODE>x</CODE> of input
+vectors and a matrix <CODE>t</CODE> of target vectors, and evaluates the gradient
+<CODE>g</CODE> of the error function with respect to the network weights (i.e.
+including the hidden unit parameters). The error
+function is sum of squares.
+Each row of <CODE>x</CODE> corresponds to one
+input vector and each row of <CODE>t</CODE> contains the corresponding target vector.
+If the output function is <CODE>'neuroscale'</CODE> then the gradient is only
+computed for the output layer weights and biases.
+
+<p><CODE>[g, gdata, gprior] = rbfgrad(net, x, t)</CODE> also returns separately 
+the data and prior contributions to the gradient. In the case of
+multiple groups in the prior, <CODE>gprior</CODE> is a matrix with a row
+for each group and a column for each weight parameter.
+
+<p><h2>
+See Also
+</h2>
+<CODE><a href="rbf.htm">rbf</a></CODE>, <CODE><a href="rbffwd.htm">rbffwd</a></CODE>, <CODE><a href="rbferr.htm">rbferr</a></CODE>, <CODE><a href="rbfpak.htm">rbfpak</a></CODE>, <CODE><a href="rbfunpak.htm">rbfunpak</a></CODE>, <CODE><a href="rbfbkp.htm">rbfbkp</a></CODE><hr>
+<b>Pages:</b>
+<a href="index.htm">Index</a>
+<hr>
+<p>Copyright (c) Ian T Nabney (1996-9)
+
+
+</body>
+</html>
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